Add Android Auto vehicle dynamics collector with COVESA VSS integration

.gitignore

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+# Built application files
+*.apk
+*.aar
+*.ap_
+*.aab
+
+# Files for the ART/Dalvik VM
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+# Java class files
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+# Generated files
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+fastlane/screenshots
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+# Version control
+vcs.xml
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+# lint/reports/
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+# Android Profiling
+*.hprof

COVESA_VSS.md

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+# COVESA Vehicle Signal Specification Integration
+
+## Overview
+
+This document explains how the Vehicle Dynamics Collector app implements concepts from the [COVESA Vehicle Signal Specification (VSS)](https://github.com/COVESA/vehicle_signal_specification).
+
+## What is COVESA VSS?
+
+The COVESA Vehicle Signal Specification is a standard for defining vehicle signals and data in a structured, hierarchical manner. It's part of the broader Software-Defined Vehicle (SDV) initiative, which aims to:
+
+- Create standardized vehicle data models
+- Enable interoperability between different automotive systems
+- Facilitate data exchange between vehicles, cloud services, and applications
+- Support the evolution toward software-defined vehicles
+
+## VSS Implementation in This App
+
+### Data Model Structure
+
+The app implements VSS-compliant data structures in `VSSDataModel.kt`:
+
+```kotlin
+VSSVehicle
+├── speed (Vehicle.Speed)
+├── acceleration (Vehicle.Acceleration)
+├── position (Vehicle.CurrentLocation)
+├── angularVelocity (Vehicle.AngularVelocity)
+└── timestamp
+```
+
+### VSS Signals Implemented
+
+#### 1. Vehicle.Speed
+- **Type**: Sensor
+- **Unit**: km/h (kilometers per hour)
+- **Source**: GPS location data
+- **Description**: Vehicle speed calculated from GPS
+
+```json
+{
+  "value": 60.5,
+  "unit": "km/h"
+}
+```
+
+#### 2. Vehicle.Acceleration
+- **Type**: Sensor
+- **Unit**: m/s² (meters per second squared)
+- **Source**: Smartphone accelerometer
+- **Components**:
+  - `longitudinal`: Forward/backward acceleration
+  - `lateral`: Left/right acceleration
+  - `vertical`: Up/down acceleration
+
+```json
+{
+  "longitudinal": 2.3,
+  "lateral": 0.1,
+  "vertical": -9.8,
+  "unit": "m/s^2"
+}
+```
+
+**Note**: The coordinate system is converted from device coordinates to vehicle coordinates:
+- Device X-axis → Vehicle lateral
+- Device Y-axis → Vehicle longitudinal
+- Device Z-axis → Vehicle vertical
+
+#### 3. Vehicle.CurrentLocation
+- **Type**: Sensor
+- **Source**: GPS
+- **Components**:
+  - `latitude`: Geographic latitude (decimal degrees)
+  - `longitude`: Geographic longitude (decimal degrees)
+  - `altitude`: Height above sea level (meters)
+  - `heading`: Direction of travel (0-360 degrees)
+  - `speed`: GPS-measured speed (m/s)
+
+```json
+{
+  "latitude": 37.7749,
+  "longitude": -122.4194,
+  "altitude": 10.0,
+  "heading": 90.0,
+  "speed": 16.8
+}
+```
+
+#### 4. Vehicle.AngularVelocity
+- **Type**: Sensor
+- **Unit**: degrees/s
+- **Source**: Smartphone gyroscope
+- **Components**:
+  - `roll`: Rotation around longitudinal axis
+  - `pitch`: Rotation around lateral axis
+  - `yaw`: Rotation around vertical axis
+
+```json
+{
+  "roll": 0.5,
+  "pitch": 1.2,
+  "yaw": 0.3,
+  "unit": "degrees/s"
+}
+```
+
+## VSS Naming Conventions
+
+The app follows VSS naming conventions:
+
+1. **Hierarchical structure**: Signals are organized in a tree structure (Vehicle.* hierarchy)
+2. **Camel case**: Property names use camelCase (e.g., `angularVelocity`)
+3. **Standard units**: Uses SI units and common automotive units
+4. **Metadata**: Each signal includes its unit of measurement
+
+## Data Collection Sessions
+
+The app extends VSS with a session concept for data collection campaigns:
+
+```kotlin
+VSSDataSession {
+  sessionId: String          // Unique identifier
+  startTime: Long           // Unix timestamp (ms)
+  endTime: Long?            // Unix timestamp (ms)
+  samples: List<VSSVehicle> // Time-series data
+  metadata: Map             // Custom session metadata
+}
+```
+
+## Future VSS Extensions
+
+The current implementation can be extended to include additional VSS signals:
+
+### Powertrain Signals
+- `Vehicle.Powertrain.FuelSystem.Level`
+- `Vehicle.Powertrain.Range`
+- `Vehicle.Powertrain.Type` (electric, hybrid, combustion)
+
+### Chassis Signals
+- `Vehicle.Chassis.SteeringWheel.Angle`
+- `Vehicle.Chassis.Brake.PedalPosition`
+- `Vehicle.Chassis.Accelerator.PedalPosition`
+
+### ADAS Signals
+- `Vehicle.ADAS.CruiseControl.IsActive`
+- `Vehicle.ADAS.LaneDepartureWarning.IsActive`
+
+### Body Signals
+- `Vehicle.Body.Lights.IsHighBeamOn`
+- `Vehicle.Body.Windshield.Wipers.Mode`
+
+### Cabin Signals
+- `Vehicle.Cabin.Infotainment.Media.Volume`
+- `Vehicle.Cabin.HVAC.Station.Temperature`
+
+## Integration with VSS Ecosystem
+
+### Data Export
+
+The JSON format used by this app can be easily converted to standard VSS formats:
+
+1. **VSS JSON**: Already compatible
+2. **VSS CSV**: Can be flattened for time-series analysis
+3. **VSS Parquet**: For big data analytics
+4. **VSS Protocol Buffers**: For efficient transmission
+
+### VSS Tools Compatibility
+
+The data structure is designed to be compatible with COVESA VSS tools:
+
+- **VSS Tools**: Python tools for VSS specification processing
+- **KUKSA.val**: VSS databroker for in-vehicle applications
+- **VSS Data Expert**: VSS visualization and analysis tools
+
+### Example Integration with KUKSA.val
+
+```python
+# Example: Converting app data to KUKSA.val format
+import json
+from kuksa_client import KuksaClientThread
+
+# Load session data
+with open('vss_session_uuid.json', 'r') as f:
+    session = json.load(f)
+
+# Connect to KUKSA.val databroker
+client = KuksaClientThread()
+client.start()
+
+# Push each sample to databroker
+for sample in session['samples']:
+    client.setValue('Vehicle.Speed', sample['speed']['value'])
+    client.setValue('Vehicle.Acceleration.Longitudinal', 
+                   sample['acceleration']['longitudinal'])
+    # ... etc
+```
+
+## VSS Benefits for This Application
+
+1. **Standardization**: Uses industry-standard signal definitions
+2. **Interoperability**: Data can be easily shared with other VSS-compliant systems
+3. **Extensibility**: Easy to add new signals following VSS hierarchy
+4. **Documentation**: Well-defined signal meanings and units
+5. **Tooling**: Can leverage existing VSS tools and libraries
+
+## Software-Defined Vehicle Concepts
+
+This app demonstrates SDV principles:
+
+1. **Sensor abstraction**: Smartphone sensors as vehicle sensors
+2. **Data standardization**: VSS-compliant data format
+3. **Software flexibility**: Easy to add/modify data collection logic
+4. **Cloud connectivity**: Ready for cloud data synchronization
+5. **Over-the-air updates**: App can be updated without hardware changes
+
+## Additional Resources
+
+- [COVESA VSS GitHub](https://github.com/COVESA/vehicle_signal_specification)
+- [VSS Documentation](https://covesa.github.io/vehicle_signal_specification/)
+- [KUKSA.val Project](https://github.com/eclipse/kuksa.val)
+- [VSS Tools](https://github.com/COVESA/vss-tools)
+- [COVESA Organization](https://covesa.global/)
+
+## Contributing VSS Signals
+
+If you add new VSS signals to this app:
+
+1. Check the [official VSS specification](https://github.com/COVESA/vehicle_signal_specification/tree/master/spec)
+2. Use the exact signal path and naming
+3. Include the correct unit of measurement
+4. Document the signal source (which sensor/API)
+5. Update this documentation
+
+## Conclusion
+
+By implementing COVESA VSS, this app:
+- Follows automotive industry standards
+- Enables integration with modern vehicle platforms
+- Demonstrates software-defined vehicle concepts
+- Provides a foundation for advanced vehicle data analytics
+
+The VSS integration makes this not just a data collection app, but a building block for the future of connected and software-defined vehicles.